Overview

Dataset statistics

Number of variables14
Number of observations344667
Missing cells54376
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.8 MiB
Average record size in memory112.0 B

Variable types

Categorical1
DateTime3
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 54376 (15.8%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.44671209)Skewed

Reproduction

Analysis started2023-10-24 11:45:46.174008
Analysis finished2023-10-24 11:46:16.374527
Duration30.2 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1.0
344667 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1034001
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 344667
100.0%

Length

2023-10-24T11:46:16.525696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T11:46:16.757015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 344667
100.0%

Most occurring characters

ValueCountFrequency (%)
1 344667
33.3%
. 344667
33.3%
0 344667
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 689334
66.7%
Other Punctuation 344667
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 344667
50.0%
0 344667
50.0%
Other Punctuation
ValueCountFrequency (%)
. 344667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1034001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 344667
33.3%
. 344667
33.3%
0 344667
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1034001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 344667
33.3%
. 344667
33.3%
0 344667
33.3%

DATUM_BESTAND
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2023-10-13 00:00:00
Maximum2023-10-13 00:00:00
2023-10-24T11:46:16.932409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:17.145741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PEILDATUM
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2023-10-01 00:00:00
Maximum2023-10-01 00:00:00
2023-10-24T11:46:17.332226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:17.739477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

JAAR
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2012-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-10-24T11:46:17.932048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:18.170519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450.14325
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:18.426840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1035.5711
Coefficient of variation (CV)2.3005369
Kurtosis55.115055
Mean450.14325
Median Absolute Deviation (MAD)8
Skewness7.5525808
Sum1.5514952 × 108
Variance1072407.6
MonotonicityNot monotonic
2023-10-24T11:46:18.731932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 48306
14.0%
313 44792
13.0%
303 39617
11.5%
330 27217
 
7.9%
316 23431
 
6.8%
308 18578
 
5.4%
306 14481
 
4.2%
324 14138
 
4.1%
301 13812
 
4.0%
304 11232
 
3.3%
Other values (18) 89063
25.8%
ValueCountFrequency (%)
301 13812
 
4.0%
302 7591
 
2.2%
303 39617
11.5%
304 11232
 
3.3%
305 48306
14.0%
306 14481
 
4.2%
307 6059
 
1.8%
308 18578
 
5.4%
310 3774
 
1.1%
313 44792
13.0%
ValueCountFrequency (%)
8418 4665
 
1.4%
8416 1051
 
0.3%
1900 229
 
0.1%
390 951
 
0.3%
389 3613
 
1.0%
362 4440
 
1.3%
361 2509
 
0.7%
335 3485
 
1.0%
330 27217
7.9%
329 904
 
0.3%
Distinct1902
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:19.431443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.352372
Min length2

Characters and Unicode

Total characters1155452
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row998
2nd row014
3rd row120
4th row044
5th row015
ValueCountFrequency (%)
101 1473
 
0.4%
402 1418
 
0.4%
403 1392
 
0.4%
301 1389
 
0.4%
201 1311
 
0.4%
203 1287
 
0.4%
401 1159
 
0.3%
404 1147
 
0.3%
409 1120
 
0.3%
802 1118
 
0.3%
Other values (1892) 331853
96.3%
2023-10-24T11:46:20.424992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 220941
19.1%
0 212332
18.4%
2 153115
13.3%
3 125060
10.8%
5 89094
7.7%
9 83186
 
7.2%
4 81859
 
7.1%
7 68060
 
5.9%
6 60341
 
5.2%
8 49768
 
4.3%
Other values (15) 11696
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1143756
99.0%
Uppercase Letter 11696
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2183
18.7%
M 1966
16.8%
B 1421
12.1%
Z 1011
8.6%
E 982
8.4%
D 767
 
6.6%
A 756
 
6.5%
F 726
 
6.2%
C 385
 
3.3%
K 377
 
3.2%
Other values (5) 1122
9.6%
Decimal Number
ValueCountFrequency (%)
1 220941
19.3%
0 212332
18.6%
2 153115
13.4%
3 125060
10.9%
5 89094
7.8%
9 83186
 
7.3%
4 81859
 
7.2%
7 68060
 
6.0%
6 60341
 
5.3%
8 49768
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1143756
99.0%
Latin 11696
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2183
18.7%
M 1966
16.8%
B 1421
12.1%
Z 1011
8.6%
E 982
8.4%
D 767
 
6.6%
A 756
 
6.5%
F 726
 
6.2%
C 385
 
3.3%
K 377
 
3.2%
Other values (5) 1122
9.6%
Common
ValueCountFrequency (%)
1 220941
19.3%
0 212332
18.6%
2 153115
13.4%
3 125060
10.9%
5 89094
7.8%
9 83186
 
7.3%
4 81859
 
7.2%
7 68060
 
6.0%
6 60341
 
5.3%
8 49768
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1155452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 220941
19.1%
0 212332
18.4%
2 153115
13.3%
3 125060
10.8%
5 89094
7.7%
9 83186
 
7.2%
4 81859
 
7.1%
7 68060
 
5.9%
6 60341
 
5.2%
8 49768
 
4.3%
Other values (15) 11696
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6188
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4091611 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:20.764582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199799062
median1.4959903 × 108
Q39.90004 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020494 × 108

Descriptive statistics

Standard deviation4.2900936 × 108
Coefficient of variation (CV)0.97299543
Kurtosis-1.7379537
Mean4.4091611 × 108
Median Absolute Deviation (MAD)1.1960002 × 108
Skewness0.46699649
Sum1.5196923 × 1014
Variance1.8404904 × 1017
MonotonicityNot monotonic
2023-10-24T11:46:21.127119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2531
 
0.7%
990004007 2482
 
0.7%
990003004 2382
 
0.7%
990004006 2007
 
0.6%
990356076 1838
 
0.5%
990356073 1705
 
0.5%
131999228 1688
 
0.5%
131999164 1664
 
0.5%
990003007 1543
 
0.4%
131999194 1514
 
0.4%
Other values (6178) 325313
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 12
< 0.1%
10501004 12
< 0.1%
10501005 12
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 12
< 0.1%
10501011 3
 
< 0.1%
11101002 11
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 173
0.1%
998418080 155
< 0.1%
998418079 39
 
< 0.1%
998418077 9
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 238
0.1%
998418073 241
0.1%
998418072 9
 
< 0.1%
998418071 9
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION 

Distinct10412
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean510.09835
Minimum1
Maximum165184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:21.471913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q3101
95-th percentile1718
Maximum165184
Range165183
Interquartile range (IQR)98

Descriptive statistics

Standard deviation3179.7084
Coefficient of variation (CV)6.2335203
Kurtosis416.13682
Mean510.09835
Median Absolute Deviation (MAD)12
Skewness16.88559
Sum1.7581407 × 108
Variance10110546
MonotonicityNot monotonic
2023-10-24T11:46:21.822781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57359
 
16.6%
2 27978
 
8.1%
3 18271
 
5.3%
4 13358
 
3.9%
5 10464
 
3.0%
6 8826
 
2.6%
7 7395
 
2.1%
8 6196
 
1.8%
9 5648
 
1.6%
10 5053
 
1.5%
Other values (10402) 184119
53.4%
ValueCountFrequency (%)
1 57359
16.6%
2 27978
8.1%
3 18271
 
5.3%
4 13358
 
3.9%
5 10464
 
3.0%
6 8826
 
2.6%
7 7395
 
2.1%
8 6196
 
1.8%
9 5648
 
1.6%
10 5053
 
1.5%
ValueCountFrequency (%)
165184 1
< 0.1%
162458 1
< 0.1%
157193 1
< 0.1%
155869 1
< 0.1%
154539 1
< 0.1%
154258 1
< 0.1%
144714 1
< 0.1%
118396 1
< 0.1%
115935 1
< 0.1%
113246 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11189
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean604.64298
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:22.151627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3111
95-th percentile1965
Maximum240002
Range240001
Interquartile range (IQR)108

Descriptive statistics

Standard deviation4104.0529
Coefficient of variation (CV)6.7875641
Kurtosis728.94494
Mean604.64298
Median Absolute Deviation (MAD)13
Skewness21.446712
Sum2.0840048 × 108
Variance16843251
MonotonicityNot monotonic
2023-10-24T11:46:22.538950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55257
 
16.0%
2 27501
 
8.0%
3 18088
 
5.2%
4 13149
 
3.8%
5 10403
 
3.0%
6 8787
 
2.5%
7 7323
 
2.1%
8 6139
 
1.8%
9 5560
 
1.6%
10 5058
 
1.5%
Other values (11179) 187402
54.4%
ValueCountFrequency (%)
1 55257
16.0%
2 27501
8.0%
3 18088
 
5.2%
4 13149
 
3.8%
5 10403
 
3.0%
6 8787
 
2.5%
7 7323
 
2.1%
8 6139
 
1.8%
9 5560
 
1.6%
10 5058
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
230940 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226321 1
< 0.1%
223891 1
< 0.1%
218673 1
< 0.1%
215131 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9333
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7647.0367
Minimum1
Maximum230661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:22.858119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile39
Q1389
median1682
Q36216
95-th percentile36610
Maximum230661
Range230660
Interquartile range (IQR)5827

Descriptive statistics

Standard deviation17904.334
Coefficient of variation (CV)2.3413427
Kurtosis34.97096
Mean7647.0367
Median Absolute Deviation (MAD)1540
Skewness5.1197928
Sum2.6356812 × 109
Variance3.2056517 × 108
MonotonicityNot monotonic
2023-10-24T11:46:23.191049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 579
 
0.2%
8 547
 
0.2%
9 523
 
0.2%
17 515
 
0.1%
12 513
 
0.1%
23 510
 
0.1%
26 497
 
0.1%
6 496
 
0.1%
25 493
 
0.1%
20 485
 
0.1%
Other values (9323) 339509
98.5%
ValueCountFrequency (%)
1 423
0.1%
2 472
0.1%
3 478
0.1%
4 463
0.1%
5 457
0.1%
6 496
0.1%
7 465
0.1%
8 547
0.2%
9 523
0.2%
10 451
0.1%
ValueCountFrequency (%)
230661 23
< 0.1%
228042 19
< 0.1%
227999 23
< 0.1%
218496 24
< 0.1%
214507 17
< 0.1%
213515 25
< 0.1%
211579 17
< 0.1%
210415 19
< 0.1%
205337 17
< 0.1%
200600 16
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10455
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11090.71
Minimum1
Maximum370139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:23.497734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q1513
median2334
Q39027
95-th percentile52174
Maximum370139
Range370138
Interquartile range (IQR)8514

Descriptive statistics

Standard deviation26871.928
Coefficient of variation (CV)2.4229222
Kurtosis38.272683
Mean11090.71
Median Absolute Deviation (MAD)2148
Skewness5.350783
Sum3.8226019 × 109
Variance7.2210053 × 108
MonotonicityNot monotonic
2023-10-24T11:46:23.821647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 444
 
0.1%
23 434
 
0.1%
6 424
 
0.1%
20 420
 
0.1%
25 418
 
0.1%
10 417
 
0.1%
31 416
 
0.1%
3 415
 
0.1%
17 402
 
0.1%
8 401
 
0.1%
Other values (10445) 340476
98.8%
ValueCountFrequency (%)
1 339
0.1%
2 355
0.1%
3 415
0.1%
4 393
0.1%
5 366
0.1%
6 424
0.1%
7 371
0.1%
8 401
0.1%
9 321
0.1%
10 417
0.1%
ValueCountFrequency (%)
370139 23
< 0.1%
365373 23
< 0.1%
348482 25
< 0.1%
347952 19
< 0.1%
344610 24
< 0.1%
341651 19
< 0.1%
323753 20
< 0.1%
315771 17
< 0.1%
310754 17
< 0.1%
298627 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean664855.73
Minimum1610
Maximum1487633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:24.158135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile42174
Q1256043
median757852
Q31026299
95-th percentile1332325
Maximum1487633
Range1486023
Interquartile range (IQR)770256

Descriptive statistics

Standard deviation420832.51
Coefficient of variation (CV)0.63296816
Kurtosis-1.1915636
Mean664855.73
Median Absolute Deviation (MAD)319272
Skewness0.0031163395
Sum2.2915383 × 1011
Variance1.771 × 1011
MonotonicityNot monotonic
2023-10-24T11:46:24.490492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880929 5102
 
1.5%
874089 4354
 
1.3%
843977 4347
 
1.3%
894305 4333
 
1.3%
880466 4273
 
1.2%
897701 4212
 
1.2%
765010 4089
 
1.2%
803534 4027
 
1.2%
777934 3984
 
1.2%
1029429 3893
 
1.1%
Other values (315) 302053
87.6%
ValueCountFrequency (%)
1610 130
 
< 0.1%
1829 138
 
< 0.1%
1920 131
 
< 0.1%
2495 173
0.1%
2507 95
 
< 0.1%
2520 190
0.1%
3561 67
 
< 0.1%
4101 168
< 0.1%
4842 312
0.1%
6805 380
0.1%
ValueCountFrequency (%)
1487633 2975
0.9%
1450392 3048
0.9%
1421705 3564
1.0%
1344277 3543
1.0%
1340587 3441
1.0%
1332325 3545
1.0%
1316373 3463
1.0%
1282936 3576
1.0%
1268857 3359
1.0%
1267069 3350
1.0%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1079684
Minimum1861
Maximum2664317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:24.833838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile46350
Q1365047
median1106917
Q31790741
95-th percentile2548229
Maximum2664317
Range2662456
Interquartile range (IQR)1425694

Descriptive statistics

Standard deviation756307.5
Coefficient of variation (CV)0.70048967
Kurtosis-0.8457335
Mean1079684
Median Absolute Deviation (MAD)703648
Skewness0.35759607
Sum3.7213145 × 1011
Variance5.7200103 × 1011
MonotonicityNot monotonic
2023-10-24T11:46:25.168617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211797 5102
 
1.5%
1281483 4354
 
1.3%
1216255 4347
 
1.3%
1315566 4333
 
1.3%
1300429 4273
 
1.2%
1341823 4212
 
1.2%
1155927 4089
 
1.2%
1205463 4027
 
1.2%
1148265 3984
 
1.2%
2491870 3893
 
1.1%
Other values (315) 302053
87.6%
ValueCountFrequency (%)
1861 130
 
< 0.1%
2097 138
 
< 0.1%
2195 131
 
< 0.1%
2628 95
 
< 0.1%
2816 173
0.1%
3288 190
0.1%
3717 67
 
< 0.1%
4898 312
0.1%
4904 168
< 0.1%
7384 380
0.1%
ValueCountFrequency (%)
2664317 3866
1.1%
2663203 3793
1.1%
2618705 3789
1.1%
2593574 3844
1.1%
2548229 3890
1.1%
2491870 3893
1.1%
2480004 3851
1.1%
2178538 3757
1.1%
2062186 3811
1.1%
2052149 1168
 
0.3%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3632
Distinct (%)1.3%
Missing54376
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean3582.5327
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-24T11:46:25.472148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1475
median1240
Q34155
95-th percentile13620
Maximum287220
Range287150
Interquartile range (IQR)3680

Descriptive statistics

Standard deviation6522.7528
Coefficient of variation (CV)1.8207099
Kurtosis139.45158
Mean3582.5327
Median Absolute Deviation (MAD)1015
Skewness7.0338726
Sum1.039977 × 109
Variance42546304
MonotonicityNot monotonic
2023-10-24T11:46:25.994043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2098
 
0.6%
105 1942
 
0.6%
110 1818
 
0.5%
185 1768
 
0.5%
180 1604
 
0.5%
300 1588
 
0.5%
140 1512
 
0.4%
175 1489
 
0.4%
125 1381
 
0.4%
165 1375
 
0.4%
Other values (3622) 273716
79.4%
(Missing) 54376
 
15.8%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 663
 
0.2%
95 734
 
0.2%
100 1015
0.3%
105 1942
0.6%
110 1818
0.5%
115 1147
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
99590 5
< 0.1%

Interactions

2023-10-24T11:46:12.162747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:53.143177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:55.808604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:58.166030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:00.575465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:02.890421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:05.021361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:07.650163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:09.966372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:12.437328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:53.428321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:56.104971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:58.445801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:00.853156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:03.139846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:05.282100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:07.930580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:10.238946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:12.705808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:53.686773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:56.373850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:58.705415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:01.107344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:03.375644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:05.527713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:08.184501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:10.470819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:12.969081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:54.098796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:56.631748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:58.991445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:01.372941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:03.606871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:05.787333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:08.474355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:10.705922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:13.236388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:54.392341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:56.876343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:59.276115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:01.630273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:03.832505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:06.256767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:08.721970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:10.942511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:13.478043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:54.678657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:57.135597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:59.508188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:01.866480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:04.049201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:06.530031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:08.955575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:11.169997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:13.745441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:54.974880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:57.399832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:59.765827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:02.121349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:04.300696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:06.798100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:09.214688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:11.419183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:14.030200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:55.252444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:57.661309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:00.055666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:02.411368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:04.548115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:07.075409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:09.473892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:11.671281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:14.280515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:55.520520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:45:57.910233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:00.310788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:02.648361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:04.784665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:07.327462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:09.720148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-24T11:46:11.909978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-24T11:46:26.216290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
BEHANDELEND_SPECIALISME_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
BEHANDELEND_SPECIALISME_CD1.0000.2150.0070.012-0.061-0.055-0.535-0.4570.050
ZORGPRODUCT_CD0.2151.000-0.139-0.148-0.176-0.207-0.363-0.3890.029
AANTAL_PAT_PER_ZPD0.007-0.1391.0000.9960.3270.3250.0840.093-0.299
AANTAL_SUBTRAJECT_PER_ZPD0.012-0.1480.9961.0000.3230.3260.0870.100-0.301
AANTAL_PAT_PER_DIAG-0.061-0.1760.3270.3231.0000.9880.3410.3240.032
AANTAL_SUBTRAJECT_PER_DIAG-0.055-0.2070.3250.3260.9881.0000.3590.3570.041
AANTAL_PAT_PER_SPC-0.535-0.3630.0840.0870.3410.3591.0000.964-0.006
AANTAL_SUBTRAJECT_PER_SPC-0.457-0.3890.0930.1000.3240.3570.9641.000-0.009
GEMIDDELDE_VERKOOPPRIJS0.0500.029-0.299-0.3010.0320.041-0.006-0.0091.000

Missing values

2023-10-24T11:46:14.646469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-24T11:46:15.445123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-10-132023-10-012013-01-01389998990089076441280513490197275284602NaN
11.02023-10-132023-10-012013-01-013890149900890933914427331055197275284602250.0
21.02023-10-132023-10-012013-01-013891209900890325119321353197275284602NaN
31.02023-10-132023-10-012013-01-01389044990089009444883751119727528460220760.0
41.02023-10-132023-10-012013-01-0138901599008906010612692313141972752846021125.0
51.02023-10-132023-10-012013-01-013891509900890552933345147881972752846022560.0
61.02023-10-132023-10-012013-01-013891309900890311759223813218193741972752846021075.0
71.02023-10-132023-10-012013-01-0138999099004000688734782197275284602NaN
81.02023-10-132023-10-012013-01-013890429900890515560648592197275284602NaN
91.02023-10-132023-10-012013-01-01389999990089009112914334519727528460220760.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3446571.02023-10-132023-10-012014-01-013031341319990721147585631142170518456253315.0
3446581.02023-10-132023-10-012014-01-0130311711949903611113557251722142170518456252875.0
3446591.02023-10-132023-10-012014-01-0130329919929905211325213355714217051845625NaN
3446601.02023-10-132023-10-012014-01-013033582011201833131514217051845625290.0
3446611.02023-10-132023-10-012014-01-0130370597900401411131614217051845625NaN
3446621.02023-10-132023-10-012020-01-013208039790012431150023751351030982183108621035.0
3446631.02023-10-132023-10-012014-01-013032221992990801176669519142170518456254085.0
3446641.02023-10-132023-10-012020-01-0131379997900301611206930141047006261870515985.0
3446651.02023-10-132023-10-012014-01-01303334990003007117963987914217051845625105.0
3446661.02023-10-132023-10-012014-01-0130336729199222112862390114217051845625NaN